How to Develop your Strategy Using Data Analytics

More and more industries are becoming data-driven due to the increasing capabilities of information technology. As businesses increasingly rely on big data and data analytics, their ability to use this information to make better business decision looks poised to become the main differentiating factor between companies that are more competitive and successful.

The main reason many companies fail to capitalize on such analytics is the flaw of asking ‘What can they do for your business’ when the questions executives and managers should be asking is ‘How do you use best data analytics and towards what goal?’ This is why it’s important to develop a strategy but, howdo you develop your strategy using data analytics?

Source Data Effectively

It’s not only necessary to gather as much data as possible to present a wide view and detailed breakdown of a business environment. Managers must also make sure all this data helps them specify any challenges hindering and opportunities available to ensure it.

Be Creative in which Data Sources you use

Towards this end, a company needs not only to gather as much analytical data as possible but the right kind as well. This means not only gather customer service data but visual, photographic and other forms of non-traditional data from social media analytics as well that helps clarify the behavior of the market or audience.

Then there are public sources of data – like those that monitor local demographics – that can help you determine how best to cater to your market. If you look at all these various unique sources alongside traditional forms of data like sales, product distribution and market trends, you can ensure that the kind of strategy you develop is more deliberate, informed and responsive. This will only increase said strategy’s effectiveness.

One challenge that managers often face when developing a strategy with all data gathered is that their company’s information technology infrastructure is not equipped to source properly, store and analyze all this information. If all your data is unstructured as well, being able to manage towards making a better decision becomes increasingly difficult.

Reorganizing your IT support system in this direction is a good start, though this process is neither simple nor swift. In the short term, executives can confer directly with chief informational officers and their subordinates to clarify which data is most relevant and important, as well as discuss actions which will collate this data efficiently as well as compensate for the kind of data your company doesn’t have the capability to gather yet.

Build Effective Data Analytic Models

It’s not enough to simply apply raw data towards driving decision making. This data need to be contextualized effectively through analytical and predictive models. The ability to predict business outcomes ahead of time helps to optimize your strategy.

Creating analytical models without a clear goal in mind is unproductive. It’s best to start out with a hypothesis and test it out rather than try discerning patterns without an overall context or goal driving this model. This will not only help you produce outcomes faster, but it will make such data easier to understand when it’s presented in practical data relationships and concepts that managers recognize.

Keep Your Model Simple

One mistake when creating data analytic models is over thinking. For example, a model too focused on statistical analysis can result in data that’s unwieldy and impractical to implement in a way that doesn’t exceed your company’s capabilities. Any model should be simplified for maximum actionability.

It’s one thing to gather data efficiently and build models to utilize said data productively. An executive must transform the culture of their companies to making relying on data to drive decisions a habit and trait of this company’s culture.

Make your Analytics Business-relevant

So much data is often acted upon in a vacuum devoid of any recognizable context. Instead, managers should sync data analytics strategy with their day-to-day process and decision-making norms already in place. Not only does this help you understand which traditional practices are counter-productive. Models should be built with the types of business decisions managers often make and use analytical tools that complement decision-making process rather than conflict with them

Integrate Analytics in Frontline Tools

This entails not only choosing the right software to analyze data but using interfaces where processes are performed that have data analysis information integrated into them.

Train Employees in Analytics Literacy to Increase Their Skills

Finally, you need personnel who are knowledgeable in using data to make decisions and drive strategy. Not only does this need training and hiring of new staff, you team leaders need to be able to model this behavior for subordinates. Also, metrics to reinforce and incentive using data analytics will help maintain momentum towards transforming a company’s culture permanently.

Conclusion

It’s crucial to develop a strategy by sourcing data effectively, building informative and predictive analytic models to determine the best course of action and transform your company’s organizational culture towards this end. Additionally, there are courses of action managers can undertake to help achieve each of these strategy-development goals. Take action today to avoid being left behind.